检测并修复文本偏斜

Question

有没有办法（使用像OpenCV这样的东西）来检测文本偏斜并通过旋转图像来纠正它？非常喜欢这个？

如果您知道角度，旋转图像看起来很容易，但对于我正在处理的图像，我不会......需要以某种方式检测它。

Answer 1

根据您的上述评论，以下是基于教程here的代码，适用于上述图片，

<强>来源

<强>旋转的

 Mat src=imread("text.png",0);
 Mat thr,dst;
 threshold(src,thr,200,255,THRESH_BINARY_INV);
 imshow("thr",thr);

  std::vector<cv::Point> points;
  cv::Mat_<uchar>::iterator it = thr.begin<uchar>();
  cv::Mat_<uchar>::iterator end = thr.end<uchar>();
  for (; it != end; ++it)
    if (*it)
      points.push_back(it.pos());

  cv::RotatedRect box = cv::minAreaRect(cv::Mat(points));
  cv::Mat rot_mat = cv::getRotationMatrix2D(box.center, box.angle, 1);

  //cv::Mat rotated(src.size(),src.type(),Scalar(255,255,255));
  Mat rotated;
  cv::warpAffine(src, rotated, rot_mat, src.size(), cv::INTER_CUBIC);
 imshow("rotated",rotated);

  

修改

另请参阅答案here，可能会有帮助。

Answer 2

这是Projection Profile方法的Python实现，用于确定偏斜。获得二进制图像后，其想法是将图像旋转各种角度并在每次迭代中生成像素的直方图。为了确定偏斜角，我们比较峰之间的最大差异，并使用该偏斜角旋转图像以校正偏斜

---

输入

结果

  

检测到的偏斜角：-5

import cv2
import numpy as np
from scipy.ndimage import interpolation as inter

def correct_skew(image, delta=1, limit=5):
    def determine_score(arr, angle):
        data = inter.rotate(arr, angle, reshape=False, order=0)
        histogram = np.sum(data, axis=1)
        score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
        return histogram, score

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] 

    scores = []
    angles = np.arange(-limit, limit + delta, delta)
    for angle in angles:
        histogram, score = determine_score(thresh, angle)
        scores.append(score)

    best_angle = angles[scores.index(max(scores))]

    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
              borderMode=cv2.BORDER_REPLICATE)

    return best_angle, rotated

if __name__ == '__main__':
    image = cv2.imread('1.png')
    angle, rotated = correct_skew(image)
    print(angle)
    cv2.imshow('rotated', rotated)
    cv2.imwrite('rotated.png', rotated)
    cv2.waitKey()

Answer 3

我会提供javacv作为参考。

package com.test13;

import org.opencv.core.*;
import org.opencv.imgproc.Imgproc;
import org.opencv.imgcodecs.Imgcodecs;

public class EdgeDetection {

    static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }

    public static void main( String[] args ) throws Exception{      
        Mat src = Imgcodecs.imread("src//data//inclined_text.jpg");
        Mat src_gray = new Mat();
        Imgproc.cvtColor(src, src_gray, Imgproc.COLOR_BGR2GRAY);
        Imgcodecs.imwrite("src//data//inclined_text_src_gray.jpg", src_gray);

        Mat output = new Mat();
        Core.bitwise_not(src_gray, output);
        Imgcodecs.imwrite("src//data//inclined_text_output.jpg", output);

        Mat points = Mat.zeros(output.size(),output.type());  
        Core.findNonZero(output, points);   

        MatOfPoint mpoints = new MatOfPoint(points);    
        MatOfPoint2f points2f = new MatOfPoint2f(mpoints.toArray());
        RotatedRect box = Imgproc.minAreaRect(points2f);

        Mat src_squares = src.clone();
        Mat rot_mat = Imgproc.getRotationMatrix2D(box.center, box.angle, 1);
        Mat rotated = new Mat(); 
        Imgproc.warpAffine(src_squares, rotated, rot_mat, src_squares.size(), Imgproc.INTER_CUBIC);
        Imgcodecs.imwrite("src//data//inclined_text_squares_rotated.jpg",rotated);    
    }
}